Published: 2023-08-30
Analyzing Customers in E-Commerce Using Dempster-Shafer Method
DOI: 10.35870/ijsecs.v3i2.1497
Downloads
Abstract
This research explores the analysis of consumer sentiment in the context of e-commerce by applying the sophisticated Dempster-Shafer method. We started with the collection of more than 20,000 consumer reviews from various leading e-commerce platforms and continued with a detailed data pre-processing stage to obtain a clean and structured dataset. Next, we leverage the Dempster-Shafer method to represent and combine information from multiple sources, addressing uncertainty in diverse consumer opinions. The results of the sentiment analysis show that the Dempster-Shafer method achieves an accuracy of around 85%, with good evaluation metrics. Additionally, this research provides insight into the factors that influence consumers' views of products or services in the growing e-commerce context. The literature review also reveals the potential application of the Dempster-Shafer method in other aspects of e-commerce business, such as risk management and consumer trust. This research highlights the contribution of the Dempster-Shafer method in addressing uncertainty and complexity in consumer sentiment analysis, yielding a deep understanding of consumer perceptions, and enabling more accurate decision making in a dynamic e-commerce context. This research also provides a foundation for further development in consumer sentiment analysis and the application of the Dempster-Shafer method in e-commerce.
Keywords
Consumer Sentiment ; E-Commerce ; Sentiment Analysis ; Dempster-Shafer Method
Article Information
This article has been peer-reviewed and published in the International Journal Software Engineering and Computer Science (IJSECS). The content is available under the terms of the Creative Commons Attribution 4.0 International License.
-
Issue: Vol. 3 No. 2 (2023)
-
Section: Articles
-
Published: %750 %e, %2023
-
License: CC BY 4.0
-
Copyright: © 2023 Authors
-
DOI: 10.35870/ijsecs.v3i2.1497
AI Research Hub
This article is indexed and available through various AI-powered research tools and citation platforms. Our AI Research Hub ensures that scholarly work is discoverable, accessible, and easily integrated into the global research ecosystem. By leveraging artificial intelligence for indexing, recommendation, and citation analysis, we enhance the visibility and impact of published research.
Erizal Nazaruddin
Management Study Program, Faculty of Economics and Business, Universitas Andalas, Padang City, West Sumatra, Indonesia
Caroline
Development Economics Study Program, Faculty of Economics and Social Sciences, Universitas Sultan Fatah, Demak Regency, Central Java Province, Indonesia
Andrijanni
Information Systems Study Program, Faculty of Science & Technology, Universitas Ottow Geissler Papua, Jayapura City, Papua Province, Indonesia
Upik Sri Sulistyawati
Entrepreneurship Study Program, Institut Teknologi dan Bisnis Muhammadiyah Bali, Jembrana Regency, Bali Province, Indonesia
-
Simbolon, N., 2023. PERANCANGAN E-COMMERCE JUAL-BELI HASIL PETERNAKAN BERBASIS WEB. Jurnal Indonesia: Manajemen Informatika dan Komunikasi, 4(3), pp.1245-1253.
-
Kiedrowsky, F.F., 2023. Sentiment Analysis Marketplaces Digital menggunakan Machine Learning. Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi), 7(3), pp.493-499.
-
Zayana, M.R., Fitri, I., Fauziah, F. and Gunaryarti, A., 2022. Penerapan Message Diggest Algorithm MD5 untuk Pengamanan Data Karyawan PT. Swifect Berbasis Desktop. Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi), 6(3), pp.386-394.
-
Saravanakumar, M. and SuganthaLakshmi, T., 2012. Social media marketing. Life science journal, 9(4), pp.4444-4451.
-
Turnbull, P.W., Leek, S. and Ying, G., 2000. Customer confusion: The mobile phone market. Journal of Marketing Management, 16(1-3), pp.143-163.
-
Safitri, R., Alfira, N., Tamitiadini, D., Dewi, W.W.A. and Febriani, N., 2021. Analisis Sentimen: Metode Alternatif Penelitian Big Data. Universitas Brawijaya Press.
-
Xu, Q.A., Chang, V. and Jayne, C., 2022. A systematic review of social media-based sentiment analysis: Emerging trends and challenges. Decision Analytics Journal, 3, p.100073.
-
Mukhtar, M. and Munawir, M., 2018. Aplikasi Decision Support System (DSS) dengan Metode Fuzzy Multiple Attribute Decission Making (FMADM) Studi Kasus: AMIK Indonesia Dan STMIK Indonesia. Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi), 2(1), pp.57-70.
-
Basiri, M.E., Naghsh-Nilchi, A.R. and Ghasem-Aghaee, N., 2014. Sentiment prediction based on dempster-shafer theory of evidence. Mathematical Problems in Engineering, 2014.
-
Maghsoudi, A., Nowakowski, S., Agrawal, R., Sharafkhaneh, A., Kunik, M.E., Naik, A.D., Xu, H. and Razjouyan, J., 2022. Sentiment Analysis of Insomnia-Related Tweets via a Combination of Transformers Using Dempster-Shafer Theory: Pre–and Peri–COVID-19 Pandemic Retrospective Study. Journal of Medical Internet Research, 24(12), p.e41517.
-
Zhang, Y., Deng, X., Wei, D. and Deng, Y., 2012. Assessment of E-Commerce security using AHP and evidential reasoning. Expert Systems with Applications, 39(3), pp.3611-3623.
-
Basiri, M.E. and Kabiri, A., 2018, April. Uninorm operators for sentence-level score aggregation in sentiment analysis. In 2018 4th International Conference on Web Research (ICWR) (pp. 97-102). IEEE.
-
Kyaw, K.S., Tepsongkroh, P., Thongkamkaew, C. and Sasha, F., 2023. Business Intelligent Framework Using Sentiment Analysis for Smart Digital Marketing in the E-Commerce Era. Asia Social Issues, 16(3), pp.e252965-e252965.
-
Panigrahi, S., Kundu, A., Sural, S. and Majumdar, A.K., 2009. Credit card fraud detection: A fusion approach using Dempster–Shafer theory and Bayesian learning. Information Fusion, 10(4), pp.354-363.
-
Zhou, L., Tang, L. and Zhang, Z., 2023. Extracting and ranking product features in consumer reviews based on evidence theory. Journal of Ambient Intelligence and Humanized Computing, 14(8), pp.9973-9983.
-
Basiri, M.E., Ghasem-Aghaee, N. and Naghsh-Nilchi, A.R., 2014. Exploiting reviewers’ comment histories for sentiment analysis. Journal of Information Science, 40(3), pp.313-328.
-
Fouladfar, F., Dehkordi, M.N. and Basiri, M.E., 2020. Predicting the helpfulness score of product reviews using an evidential score fusion method. IEEE Access, 8, pp.82662-82687.
-
Song, B., Yan, W. and Zhang, T., 2019. Cross-border e-commerce commodity risk assessment using text mining and fuzzy rule-based reasoning. Advanced Engineering Informatics, 40, pp.69-80.
-
Huang, D. and Xu, S., 2023. A Transaction Frequency Based Trust for E-Commerce. Computers, Materials & Continua, 74(3).
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Authors who publish with this journal agree to the following terms:
1. Copyright Retention and Open Access License
Authors retain copyright of their work and grant the journal non-exclusive right of first publication under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
This license allows unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
2. Rights Granted Under CC BY 4.0
Under this license, readers are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose, including commercial use
- No additional restrictions — the licensor cannot revoke these freedoms as long as license terms are followed
3. Attribution Requirements
All uses must include:
- Proper citation of the original work
- Link to the Creative Commons license
- Indication if changes were made to the original work
- No suggestion that the licensor endorses the user or their use
4. Additional Distribution Rights
Authors may:
- Deposit the published version in institutional repositories
- Share through academic social networks
- Include in books, monographs, or other publications
- Post on personal or institutional websites
Requirement: All additional distributions must maintain the CC BY 4.0 license and proper attribution.
5. Self-Archiving and Pre-Print Sharing
Authors are encouraged to:
- Share pre-prints and post-prints online
- Deposit in subject-specific repositories (e.g., arXiv, bioRxiv)
- Engage in scholarly communication throughout the publication process
6. Open Access Commitment
This journal provides immediate open access to all content, supporting the global exchange of knowledge without financial, legal, or technical barriers.